Summary A number of web applications provide completely automated machine translation services, allowing users to easily translate information of interest. However, these services still generate inaccurate results when translating technical terminologies. Therefore, we propose a new method that collects reliable pairs of English–Korean technical terms and translates the given English terminology to Korean. To collect the pairs, we utilize textual big data, such as Korean academic papers, and develop a new statistical model to determine appropriate characteristics. Our method is evaluated in terms of the reliability of English–Korean pairs and the precision of translation. We thus confirm that our method can produce highly reliable data and can positively influence the translation quality of technical terminologies. Copyright © 2014 John Wiley & Sons, Ltd.

Intelligent technologies and applications for big data analytics Data are being generated at a phenomenal and ever growing rate.It comes from numerous sources including distributed sensors, social media, images and text documents.This 'tsunami of data' needs to be stored and analysed speedily and efficiently.Traditional approaches to managing data are simply not powerful and capable enough to handle the large data volumes.The characteristics and features of such big data, such as data from social media or generated by ambient sensors, are entirely different in nature from traditional enterprise data or information.New solutions and computational paradigms which allow us to analyse, understand, store, retrieve and visualize big data are badly needed.They are fostering new research directions in computer science and technology, and in the cognitive sciences.In addition to collection, storage, processing and retrieval of large quantities of data, some research seeks to understand the mechanisms by which humans or intelligent systems can absorb such information and apply that information to solve complex problems or provide different services.Obviously, technologies which support storing and analysis of large quantities of data must also meet important requirements for secure operation to guarantee the confidentiality and the integrity of the collected data.Such features can be ensured thanks to new paradigms of distributed computing and the development of specialized infrastructure attached to the service sector that collects and processes shared data in big repositories or in the cloud.Secure collection and intelligent management of data are now clearly a part of many domains and aspects of our life and an important part of scientific research associated with ambient technologies and ubiquitous computing.In view of the many interesting research topics in the rapidly developing field of Big Data, we decided to produce this Special Issue on 'Intelligent Technologies and Applications for Big Data Analytics'.The general aim of this special issue is to document the state of the art in intelligent and advanced approaches to the entire management cycle for big data.From the several dozen papers submitted to this Special Issue, eight articles of particular interest were selected for publication.In our view, they present really interesting solutions to some problems in the areas covered by this issue.• 'BGG: A Graph Grammar Approach for Modelling the Software Architecture of Big Data-Oriented Software Systems', by Chen Li et al.The authors propose a Breeze Graph Grammar (BGG) to model the software architecture in both its static and dynamic aspects.A BGG model is used for reliability modeling and evaluation of the software.The idea is developed in three directions.First, a BGG definition is used to specify the software architecture and map the system dynamic evolution to BGG transformation rules.Second, a BGG reliability model is proposed in which error attributes are added to the BGG graph to capture the system error information, and system error state transitions are modeled with BGG graph rewriting rules.Finally, the authors present rules to map the BGG reliability model to a Generalized Stochastic Petri Net (GSPN) model, which can be used for reliability evaluation.• 'Making sense from Big RDF Data: OUSAF for measuring Ontology Usage' by Jamshaid Ashraf et al. The authors propose the Ontology USage Analysis Framework (OUSAF). OUSAF provides a methodological approach to performing the various phases such as identifying, analysing, representing and utilizing the Ontology usage results from Big RDF Data.The paper describes

The most fundamental step in semantic information processing (SIP) is to construct knowledge base (KB) at the human level; that is to the general understanding and conception of human knowledge. WordNet has been built to be the most systematic and as close to the human level and is being applied actively in various works. In one of our previous research, we found that a semantic gap exists between concept pairs of WordNet and those of real world. This paper contains a study on the enrichment method to build a KB. We describe the methods and the results for the automatic enrichment of the semantic relation network. A rule based method using WordNet's glossaries and an inference method using axioms for WordNet relations are applied for the enrichment and an enriched WordNet (E-WordNet) is built as the result. Our experimental results substantiate the usefulness of E-WordNet. An evaluation by comparison with the human level is attempted. Moreover, WSD-SemNet, a new word sense disambiguation (WSD) method in which E-WordNet is applied, is proposed and evaluated by comparing it with the state-of-the-art algorithm.

Real-time systems have to complete the execution of a task within the predetermined time while ensuring that the execution results are logically correct. Such systems require scheduling methods that can adequately distribute the given tasks to a processor. Scheduling methods that all tasks can be executed within a predetermined deadline are called an optimal scheduling. In this paper, we propose a new and simple scheduling algorithm (LSTR: least slack time rate first) as a dynamic-priority algorithm for a multi-processor environment and demonstrate its optimal possibility through various tests.

This article deals with research that automatically constructs a lexical dictionary of unknown words. The lexical dictionary has been usefully applied to various fields for semantic information processing. It has limitations in which it only processes terms defined in the dictionary. Under this circumstance, the concept of “Unknown Word (UW)” is defined. UW, in this research, is considered a word not defined in WordNet. Here is where a new method to construct UW lexical dictionary through inputting various document collections scattered on the web is proposed. We grasp related terms of UW and measure semantic relatedness (similarity) between an UW and a related term(s). The relatedness is obtained by calculating both probabilistic relationship and semantic relationship. This research can extend UW lexical dictionary with an abundant number of UW. It is also possible to prepare a foundation for semantic retrieval by simultaneously using the UW lexical dictionary and WordNet.